An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and UAV Flights
2.2. DSM and Orthomosaic Generation
2.3. Image Analysis Algorithm Description
2.3.1. Plot Field Analysis
- Object height calculation: A chessboard segmentation algorithm was used to segment the DSM of every sub-parcel into squares of 0.5 m. Then, the minimum height above sea level was calculated, to build the digital terrain model (DTM), i.e., a graphical representation of the terrain height. Next, the orthomosaic, containing the spectral and height information, was segmented, using the multiresolution segmentation algorithm (known as MRS) [31] (Figure 4). MRS is a bottom-up segmentation algorithm based on a pairwise region merging technique in which, on the basis of several parameters defined by the operator (scale, color/shape, smoothness/compactness), the image is subdivided into homogeneous objects.The scale parameter was fixed at 17, as obtained in a previous study, in which a large set of herbaceous crop plot imagery was tested by using a tool developed for scale value optimization, in accordance with [32]. This tool was implemented as a generic tool for the eCognition software in order to parameterize multi-scale image segmentation, enabling objectivity and automation of GEOBIA analysis. In addition, shape and compactness parameters were fixed to 0.4 and 0.5, respectively, to be well suited for vegetation detection by using UAV imagery [33].Once the objects were created, the height above the terrain was extracted from the Crop Height Model (CHM), which was calculated by subtracting the DTM from the DSM.
- Shadow removing: shadows were removed by using an overall intensity measurement given by the brightness feature, as shadows are less bright than vegetation and bare soil [34]. The 15th percentile brightness was used as the threshold to accurately select and remove shadow objects, based on previous studies (Figure 4).
- Vegetation thresholding: Once homogenous objects were created by MRS, vegetation (crop and weeds) objects were separated from bare soil by using the NIR/G band ratio. This is an easy to implement ratio, designed to detect structural and color differences in land classes [35], and is insensitive to soil effects, e.g., differences observed in this ratio have been used to separate vegetation and bare soil, as NIR reflectance is higher for vegetation than for bare soil, while more similar spectral values are shown for vegetation and bare soil when considering a broad waveband in the green region, which enhances these diferences [36]. The optimum ratio value for vegetation distinction was conducted using an automatic and iterative threshold approach, following the Otsu method [37], implemented in eCognition, in accordance with Torres-Sánchez et al. [28].
- Crop row detection: A new level was created to define the main orientation of the crop rows, where a merging operation was performed to create lengthwise vegetation objects following the shape of a crop row. In this operation, two candidate vegetation objects were merged only if the length/width ratio of the target object increased after the merging. Next, the largest-in-size object, with orientation close to row one, was classified as a seed object belonging to a crop row. Finally, the seed object grew in both directions, following the row orientation, and a looping merging process was performed until all the crop rows hit the parcel limits. This procedure is fully described in Peña et al. [38]. After a stripe was classified as a sunflower (or cotton) crop-line, the separation distance between rows was used to mask the adjacent stripes, to avoid misclassifying weed infestation as crop rows (Figure 4).
- Sample pre-classification: Once vegetation objects were identified, the algorithm searched for potential samples of crops and weeds in every sub-parcel. Inside the row, vegetation objects with an above average height, previously determined for each crop row as the average height of plants in a row, were pre-classified as crops. All vegetation objects outside the crop rows were classified as weeds (Figure 4). Weeds growing inside the row, regardless of height, could be properly classified in the next step.
2.3.2. RF Training Set Selection and Classification
2.3.3. Classification Enhancement
2.3.4. Prescription Map Generation
2.4. Crop Height Validation
2.5. Weed Detection Validation
3. Results and Discussion
3.1. OBIA-Based Crop Height Estimations
3.2. Automatic RF Training Set Selection and Classification
3.3. Weed Detection
3.4. Prescription Maps
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Crop | Name | Flight Date | Crop Row Separation (m) | Area (ha) | Weed Validation Frames |
---|---|---|---|---|---|
Sunflower | S1–16 | June 2016 | 0.8 | 4.22 | 107 |
S1–17 | May 2017 | 0.7 | 1.03 | 26 | |
S2–17 | May 2017 | 0.7 | 1.56 | 37 | |
Cotton | C1–16 | June 2016 | 0.95 | 1.13 | 31 |
C2–16 | June 2016 | 0.95 | 1.05 | 27 |
Trainning Set Data | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Selected Training Objects (%) * | Selected Training Area over Total Field Area Φ | |||||||||
Field | Flight Altitude | Bare Soil | Crop | Shadow | Weed | Bare Soil | Crop | Shadow | Weed | Total |
C1–16 | 30 | 23 | 28 | 27 | 22 | 1.0 | 0.9 | 0.7 | 0.5 | 3.0 |
C2–16 | 30 | 23 | 28 | 28 | 22 | 0.6 | 0.5 | 0.5 | 0.3 | 1.8 |
S1–16 | 30 | 19 | 28 | 30 | 23 | 1.4 | 1.1 | 1.1 | 0.8 | 4.2 |
60 | 17 | 32 | 36 | 16 | 2.6 | 2.0 | 1.4 | 0.5 | 6.5 | |
S1–17 | 30 | 21 | 28 | 29 | 23 | 1.5 | 1.3 | 1.2 | 0.9 | 4.9 |
60 | 18 | 30 | 33 | 19 | 2.9 | 1.7 | 1.7 | 0.8 | 7.1 | |
S2–17 | 30 | 20 | 28 | 29 | 23 | 1.4 | 1.4 | 1.4 | 1.0 | 5.2 |
60 | 18 | 30 | 33 | 19 | 2.5 | 1.8 | 1.7 | 0.9 | 6.8 |
Crop | Field | WdA (%) |
---|---|---|
Cotton | C1–16 | 84.0 |
C2–16 | 63.0 | |
Sunflower | S1–16 | 59.1 |
S1–17 | 87.9 | |
S2–17 | 81.1 |
Crop | Field | Herbicide Saving (%) |
---|---|---|
Cotton | C1–16 | 60 |
C2–16 | 79 | |
Sunflower | S1–16 | 27 |
S1–17 | 37 | |
S2–17 | 28 |
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De Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery. Remote Sens. 2018, 10, 285. https://doi.org/10.3390/rs10020285
De Castro AI, Torres-Sánchez J, Peña JM, Jiménez-Brenes FM, Csillik O, López-Granados F. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery. Remote Sensing. 2018; 10(2):285. https://doi.org/10.3390/rs10020285
Chicago/Turabian StyleDe Castro, Ana I., Jorge Torres-Sánchez, Jose M. Peña, Francisco M. Jiménez-Brenes, Ovidiu Csillik, and Francisca López-Granados. 2018. "An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery" Remote Sensing 10, no. 2: 285. https://doi.org/10.3390/rs10020285
APA StyleDe Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O., & López-Granados, F. (2018). An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery. Remote Sensing, 10(2), 285. https://doi.org/10.3390/rs10020285